ReviewiScience2021
Prediction of RNA subcellular localization: Learning from heterogeneous data sources.
Review in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Article
- MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale.Frontiers in bioinformatics · 2024Article
- LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism.Bioinformatics (Oxford, England) · 2023Article
- RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies.Briefings in bioinformatics · 2023Review
- Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury.Nature communications · 2023Article
- EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction.Computational and structural biotechnology journal · 2022Article
- Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics.Frontiers in bioinformatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ hybridization-based techniques. Although such imaging data are ideal for characterization of RNA distribution, these techniques remain costly, time-consuming, and technically challenging. Given these limitations, imaging data exist only for a limited number of RNAs. We argue that the field of RNA localization would greatly benefit from complementary techniques able to characterize location of RNA. Here we discuss the importance of RNA localization and the current methodology in the field, followed by an introduction on prediction of location of molecules. We then suggest a machine learning approach based on the integration between imaging localization data and sequence-based data to assist in characterization of RNA localization on a transcriptome level.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.